This talk lays out a series of real-life case studies that illustrate a few useful “tricks of the trade” for successful design of experiments. These techniques are not widely used, but all of them have proven quite useful and worth knowing. The techniques include:
- Using standard error to constrain optimization
- Employing Cpk (or Ppk) to optimize your DOE
- Combining categoric factors to avoid improbable combinations
- Applying mean bias correction to a transformed response
Get ready for some practical advice on DOE that will enhance your use and/or consulting on this powerful statistical tool.

A Lean Six Sigma Journey at the University of Strathclyde: Results Achieved to Date, Key Lessons Learned and Future Directions

Lean Six Sigma (LSS) is a powerful methodology for achieving process efficiency and effectiveness resulting in enhanced customer satisfaction and improved bottom line results. Although a number of manufacturing and service organisations are utilising the power of this integrated methodology, it has been clear through the authors’ research that the Higher Education Institutions (HEIs) are far behind in the introduction and development of this Process Excellence (PE) methodology. A number of HEIs have embarked on the Lean initiative for the past 6 to 7 years but they are not so keen in integrating Six Sigma principles for understanding and analysing variation within the University business processes. In the authors’ opinion, HEIs can use both methodologies simultaneously depending upon the nature of the problem at hand. Moreover we firmly believe Six Sigma methodology (Define-Measure-Analyse-Improve-Control) can be very effective in solving various business problems in University processes where the solutions are unknown or root causes are never determined in a true sense.

The purpose of the paper is to critically evaluate Lean Six Sigma (LSS) as a powerful business improvement methodology for improving the efficiency and effectiveness of Higher Education Institutions (HEIs). The paper will explore the fundamental challenges and critical success factors encountered in the introduction and development of LSS at the University of Strathclyde, Glasgow, Scotland. The paper will then present the role of tools and techniques for the sustainability of this initiative for making the HEIs (in general) more efficient and effective. The final part of the paper will illustrate the type of projects completed by the staff members at the University of Strathclyde as part of the LSS journey. This paper makes an attempt to remove the myth that LSS is confined to manufacturing. It also demonstrates through relevant existing literature and authors’ experiences that LSS is equally applicable to public sector organisations and in particular HEIs. Although Lean has been adopted by few HEIs in the UK and abroad, very few HEIs have adopted the integrated LSS approach for waste reduction and variability reduction which leads to superior performance and enhanced student satisfaction.

Gaussian processes provide a popular statistical modelling approach in various fields, including spatial statistics and computer experiments. Strategic experimental design could prove crucial when data are hard to collect. We use the Karhunen-Loeve decomposition to study several popular design criteria. The resulting expressions are useful for understanding and comparing the criteria. A truncated form of the expansion is used to generate optimal designs. We give detailed results, including an error analysis, for the well-established Integrated Mean Squared Prediction Error.

During a Phase I analysis, the available in-control data is used to estimate the required model parameters, which, in turn, are used for control chart design. If we are concerned with serially independent attributes data stemming from a Poisson or binomial distribution, then parameter estimation is essentially based on the sample mean. Assume now that the Phase I data is incomplete, either because some observations were missing or invalid right from the beginning, or because some observations had to be excluded during the Phase I analysis since they were identified as outliers. Then the estimation has to be done from the incomplete data, which is a simple task in the case of independence, because then, the estimates are computed from the reduced data set in the same way as from the full data set (e.g., still based on the mean in the above attributes cases).

If the in-control data are assumed to stem from a time-dependent (though stationary) process, then missing observations severely affect the process of parameter estimation. We consider the case of a Poisson INAR(1) process and a binomial AR(1) process, where the monitored attributes have a first order autoregressive dependence structure. We describe approaches of how to estimate the model parameters in the case of missing observations, and we analyze the performance of these estimators in simulation experiments. Then the ARL performance of some control charts based on such estimated parameters is investigated, also in the case when outliers are not removed from the Phase I data. A real-data example is considered for illustrative purposes.

Recommendations on Kaizen Kobetsu and Six Sigma Implementations from an Industrial Perspective

In today's competitive environment, daily increases in business excellence and competitive strength is the common goal of all activities (processes) carried out by all competitive industrial sectors. Industrial organizations’ perspective on problems appears to be the most important determining factor when seeking motivation to identify and solve problems. Kaizen kobetsu is a Japanese approach, consisting of a number of systematic studies in which employees work by a specific plan focusing on a problem, detecting its source and according to its type, develop and test countermeasures to eliminate it. The 6 Sigma uses experience as well as process information and data to put forward cause-effect relationships, determine the problems (variability) source and develop the appropriate solution. These two approaches are applied in our factory to solve problems occurring in our assembly process. We've brought forward recommendations to identify the strengths and weaknesses, and get more effective results from each approach.

The Weibull distribution is a frequently preferred model in the analysis of lifetime data of technical products. In this context, the fitting of the distribution to data by parameter estimation is usually referred to as Weibull Analysis. If this is applied to warranty data, the problem of heavy censoring is faced as such data contain information excusively about the warranty period which covers in general only the initial phase of the useful life period. In addition, the proportion of failing products is small compared to the population.
This presentation discusses the effects of heavily censored data on the parameter estimation of the Weibull distribution. It should provide a guidline about what aspects to consider when the question about lifetime estimation based on warranty data is raised.